Virtual machine management method using virtual machine deployment simulation

ABSTRACT

Provided is a virtual machine (VM) management method of simulating a change in deployment of VMs deployed on physical servers including a first physical server and a second physical server physically separated from the first physical server and scheduling deployment of VMs and predicting workload of VMs.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority under 35 U.S.C. § 119(b) to KoreanPatent Application No. 10-2019-0092851, filed on Jul. 31, 2019, KoreanPatent Application No. 10-2019-0101200, filed on Aug. 19, 2019, andKorean Patent Application No. 10-2019-0110079, filed on Sep. 5, 2019,the disclosures of which are incorporated herein by reference in theirentireties.

BACKGROUND 1. Field of the Invention

The present invention relates to a virtual machine management method anda virtual machine management system implementing the same and, moreparticularly, to a method of effectively processing and managing avirtual machine and a virtual machine management system for implementingthe method.

2. Discussion of Related Art

Data centers which consume about 2% of global electric power are highdensity and high energy-consuming buildings which use the most poweramong individual buildings. However, data centers are managed in aconservative manner in which stability is the top priority and thus arebeing inefficiently run at the present time. In the future, due tospread of the Internet of Things (IoT), big data, cloud technology,etc., small-scale data centers are expected to be integrated intohyperscale or mega data centers which are large-scale data centers. Forthis reason, data center running and management are also expected to becomplicated due to extension of infrastructure. Consequently, it is veryimportant to maximize the efficiency of running a data center byswitching unnecessary servers to an idle mode or a power-down mode andmanaging available servers as stably as possible, and also it is veryimportant to appropriately deploy numerous virtual machines in a datacenter so as to reduce operational cost by reducing power consumption.

In a virtualization environment in which physical machines (PMs) areconfigured as virtual machines (VMs), running VMs may be moved to adifferent PM, which is referred to as live migration. This is anecessary management method for efficiently managing a data center.

Technology for running a data center by migrating VMs (Japanese PatentPublication No. 5827594, registered on Oct. 23, 2015) has been developedand released to efficiently manage a data center. However, there is aninconvenience in that it is not possible to predict results beforeapplying a management program to a data center.

Also, according to the related technology, VMs are migrated on the basisof a specific time point, and thus a specific server may be overloaded.

Further, technologies which have been released thus far relate to datacenter running, and it is not possible to predict results beforeapplying a management program to a data center.

SUMMARY OF THE INVENTION

The present invention is directed to providing a virtual machine (VM)management method of managing VMs on physical servers with maximumefficiency and a VM management system for implementing the VM managementmethod.

Objects to be achieved by embodiments of the present invention are notlimited thereto, and objects not mentioned above will be clearlyunderstood by those of ordinary skill in the art from this specificationand the accompanying drawings.

According to an aspect of the present invention, there is provided a VMworkload prediction method implemented by a prediction device tocalculate a prediction load, which is a load on a physical serverpredicted to be imposed by a plurality of VMs installed on the physicalserver, the method including: collecting storage load information whichis information related to a storage load corresponding to a load on thephysical server caused by the VMs installed on the physical server,classifying storage loads of the VMs into groups by a predeterminedclassification method, when the storage loads are arranged on the basisof an arrangement reference which is a certain reference, forming aprediction model for calculating a prediction load from storage loadsclassified into each group, selecting at least one of a plurality ofprediction models by a predetermined selection method to increaseprediction accuracy of a target prediction load which is a load on thephysical server predicted to be caused by the a target VM to beanalyzed, and calculating target prediction load information which isinformation related to the target prediction load according to apredetermined calculation method on the basis of target storage loadinformation, which is information related to a target storage loadcorresponding to a load on the physical server caused by the target VM,and a selection model which is the selected prediction model.

The predetermined selection method may include a predetermined firstselection method of selecting, when the target storage load is locatedin a group of storage loads after the target storage load is arranged onthe basis of the arrangement reference, a prediction model produced onthe basis of the storage loads classified into the group in which thetarget storage load is located as the selection model.

The predetermined selection method may include a predetermined secondselection method of selecting, when the target storage load is notlocated in any group of storage loads after the target storage load isarranged on the basis of the arrangement reference, prediction modelscorresponding to groups of storage loads satisfying a predeterminedcondition as the selection model.

The predetermined condition may require the group to be located within acertain distance from the target storage load when the target storageload is arranged on the basis of the arrangement reference.

The predetermined condition may require the group to be located within areference range from a reference distance which is the shortest one ofdistances from the target storage load to the groups of storage loadswhen the target storage load is arranged on the basis of the arrangementreference.

The predetermined calculation method may be a method of calculating thetarget prediction load information in different ways depending onwhether the selection model is singular or plural.

The predetermined calculation method may be a method of calculating thetarget prediction load information by inputting the target storage loadinformation to the selection model when the selection model is singular.

The predetermined calculation method may be a method of calculating,when the selection model has a plurality of selection models, the targetprediction load information by considering weights calculated on thebasis of a ratio of separation distances from the target storage load tostorage load groups corresponding to the selection models after thetarget storage load is arranged on the basis of the arrangementreference.

The predetermined calculation method may be a method of calculating thetarget prediction load information for a certain time period.

According to another aspect of the present invention, there is provideda VM deployment method of deploying a plurality of VMs on a firstphysical server and a second physical server to efficiently run aphysical server, on which VMs are installed, including the firstphysical server and the second physical server, the VM deployment methodincluding: calculating VM workload information, which is informationrelated to VM workloads corresponding to loads on the physical serversimposed by the VMs installed on the physical servers, according to apredetermined workload calculation method on the basis of informationrelated to operation of the first physical server and the secondphysical server, calculating initially predicted virtual loadinformation which is information related to an initially predictedvirtual load associated with loads on the physical servers predicted tobe imposed by the VMs during a predetermined first time period on thebasis of the VM workloads, calculating deployment schedule information,which is information related to a deployment schedule of the VMs on thefirst physical server and the second physical server, according to afirst predetermined machine deployment calculation method, and migratingand deploying the VMs on the first physical server and the secondphysical server so that the VMs are deployed on the first physicalserver and the second physical server according to the deploymentschedule calculated by the first predetermined machine deploymentcalculation method. The first predetermined machine deploymentcalculation method may be a method of calculating the deploymentschedule of the VMs to minimize an objective function value on the basisof the initially predicted virtual load information.

The objective function may include a first objective function which is afunction related to a load variation of a physical server calculatedwith the initially predicted virtual load during the predetermined firsttime period.

The objective function may include a second objective function which isa function related to a difference between loads on physical serverscalculated with the initially predicted virtual load during thepredetermined first time period.

The objective function may include a third objective function which is afunction related to the number of times that the VMs are migrated on thefirst physical server and the second physical server to be deployedaccording to the deployment schedule.

The objective function may include a fourth objective function which isa function related to whether the first physical server operates duringthe predetermined first time period and whether the second physicalserver operates during the predetermined first time period.

The objective function may include a first objective function which is afunction related to a load variation of a physical server calculatedwith the initially predicted virtual load during the predetermined firsttime period, a second objective function which is a function related toa difference between loads on physical servers calculated with theinitially predicted virtual load during the predetermined first timeperiod, a third objective function which is a function related to thenumber of times that the VMs are migrated on the first physical serverand the second physical server to be deployed according to thedeployment schedule, and a fourth objective function which is a functionrelated to whether the first physical server operates during thepredetermined first time period and whether the second physical serveroperates during the predetermined first time period.

The deployment schedule may include a first deployment schedule which isa deployment of the VMs on the first physical server and the secondphysical server for minimizing a value of the second objective function,a value of the third objective function, and a value of the fourthobjective function when the first objective function has a minimumvalue, a second deployment schedule which is a deployment of the VMs onthe first physical server and the second physical server for minimizinga value of the first objective function, the value of the thirdobjective function, and the value of the fourth objective function whenthe second objective function has a minimum value, a third deploymentschedule which is a deployment of the VMs on the first physical serverand the second physical server for minimizing the value of the firstobjective function, the value of the second objective function, and thevalue of the fourth objective function when the third objective functionhas a minimum value, and a fourth deployment schedule which is adeployment of the VMs on the first physical server and the secondphysical server for minimizing the value of the first objectivefunction, the value of the second objective function, and the value ofthe third objective function when the fourth objective function has aminimum value.

The predetermined workload calculation method may include a method ofcalculating the VM workload information on the basis of only the degreeof operation of a central processing unit (CPU).

The first predetermined machine deployment calculation method mayinclude a method of calculating the deployment schedule of the VMs sothat a load may be assigned to an arbitrary physical server only withina predetermined range.

The first predetermined machine deployment calculation method mayinclude a method of moving at least one of VMs deployed on the arbitraryphysical server to another physical server so that a load may beassigned to the arbitrary physical server only within the predeterminedrange when a load on the arbitrary physical server is predicted to be afirst reference or more.

The first predetermined machine deployment calculation method mayinclude a method of moving all VMs deployed on the arbitrary physicalserver to another physical server so that a load may be assigned to thearbitrary physical server only within the predetermined range when aload on the arbitrary physical server is predicted to be a secondreference or less.

The first predetermined machine deployment calculation method mayinclude a method of calculating the deployment schedule information sothat an operating physical server may not be switched to a non-operatingstate due to migration of a VM which causes a non-operating physicalserver to operate.

The VM deployment method may further include determining, when the VMsare deployed on the first physical server and the second physical serveraccording to the deployment schedule, whether selectively predictedvirtual load information, which is information related to a selectivelypredicted virtual load associated with predicted loads on the physicalservers, is calculated normally according to a predetermined checkmethod on the basis of the selectively predicted virtual loadinformation and actual virtual workload information which is informationrelated to an actual workload corresponding to a load actually imposedon the physical servers by the VMs.

The predetermined check method may be a method of determining that theselectively predicted virtual load is calculated inaccurately when adifference between the actual workload of the VMs and the selectivelypredicted virtual load information corresponding thereto is a certainvalue or more.

According to another aspect of the present invention, there is provideda VM deployment device for implementing a VM deployment method ofdeploying a plurality of VMs on a first physical server and a secondphysical server to efficiently run a physical server, on which VMs areinstalled, including the first physical server and the second physicalserver, the VM deployment device including: a processor and a storageconfigured to store a program for performing the VM deployment method.The VM deployment method includes calculating VM workload information,which is information related to VM workloads corresponding to loads onthe physical servers imposed by the VMs installed on the physicalservers, according to a predetermined workload calculation method on thebasis of information related to operation of the first physical serverand the second physical server, calculating initially predicted virtualload information which is information related to an initially predictedvirtual load associated with loads on the physical servers predicted tobe imposed by the VMs during a predetermined first time period on thebasis of the VM workloads, calculating deployment schedule information,which is information related to a deployment schedule of the VMs on thefirst physical server and the second physical server, according to afirst predetermined machine deployment calculation method, and migratingand deploying the VMs on the first physical server and the secondphysical server so that the VMs are deployed on the first physicalserver and the second physical server according to the deploymentschedule calculated by the first predetermined machine deploymentcalculation method. The first predetermined machine deploymentcalculation method may be a method of calculating the deploymentschedule of the VMs to minimize an objective function value on the basisof the initially predicted virtual load information.

According to another aspect of the present invention, there is provideda VM deployment simulation method of simulating a change in deploymentof VMs deployed on physical servers including a first physical serverand a second physical server physically separated from the firstphysical server, the VM deployment simulation method including:calculating observed state information which is information related tostates of the first physical server and the second physical serverduring a first time period and calculated on the basis of informationthat is stored in a storage and related to operation of the firstphysical server and the second physical server, calculating selecteddeployment information related to a selected deployment which is adeployment selected as a new deployment of the VMs on the first physicalserver and the second physical server, and calculating comparativeinformation on the basis of the observed state information and predictedstate information, which is information related to states of the firstphysical server and the second physical server during a second timeperiod and calculated on the basis of the information that is stored inthe storage and related to operation of the first physical server andthe second physical server, to compare a case in which the VMs aredeployed in the selected deployment selected on the basis of thepredicted state information with a case in which the VMs are deployed ina past deployment corresponding to a deployment of the VMs which is abase of the observed state information.

The observed state information may include at least one of observedoccupancy information which is information related to loads on thephysical servers imposed by the VMs during the first time period,observed violation information which is information related to whether apredetermined reference is violated when the VMs run on the physicalservers during the first time period, observed idle state informationwhich is information related to a time in which the physical servers donot operate because the VMs are not deployed on the physical serversduring the first time period, and observed migration information whichis information related to the number of times that the VMs are migratedduring the first time period, and the predicted state information mayinclude at least one of predicted occupancy information which isinformation related to loads on the physical servers predicted to beimposed by the VMs during the second time period, predicted violationinformation which is information related to whether the predeterminedreference is violated when the VMs are assumed to run in an arbitrarydeployment on the physical servers during the second time period,predicted idle state information which is information related to a timein which the physical servers do not operate because the VMs are notdeployed on the physical servers during the second time period, andpredicted migration information which is information related to thenumber of times that the VMs are migrated during the second time period.

The VM deployment simulation method may further include calculatingeconomic efficiency information which is information related to economicbenefit acquired when the VMs are deployed in the selected deployment inrelation to a case in which the VMs are deployed in the past deployment.

The economic efficiency information may be calculated on the basis ofthe observed idle state information and the predicted idle stateinformation.

The VM deployment simulation method may further include calculatinginitially predicted virtual load information, which is informationrelated to an initially predicted virtual load associated with the loadson the physical servers predicted to be imposed by the VMs during thesecond time period, using the information stored in the storage andrelated to operation of the first physical server and the secondphysical server according to a predetermined prediction method.

The VM deployment simulation method may further include calculatingmachine deployment information, which is information related to amachine deployment corresponding to a deployment of the VMs on the firstphysical server and the second physical server calculated according to apredetermined machine deployment calculation method, and thepredetermined machine deployment calculation method may be a method ofcalculating a plurality of machine deployments, which are deployments ofthe VMs when values of a plurality of objective functions are minimized,on the basis of the initially predicted virtual load information. Theselected deployment may be one of the machine deployments.

The VM deployment simulation method may further include calculatingcandidate predicted state information which is information related to avalue within a range of a maximum to a minimum of predicted state valuescalculated on the basis of a plurality of pieces of predicted stateinformation and displaying the predicted state value and a candidatepredicted state value calculated on the basis of the candidate predictedstate information according to a predetermined display method. Thepredetermined display method may be a method of displaying the predictedstate value and a candidate predicted state value as a certain range.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, characteristics and advantages of thepresent invention will become more apparent to those of ordinary skillin the art by describing exemplary embodiments thereof in detail withreference to the accompanying drawings, in which:

FIG. 1 shows a conceptual diagram of a virtual machine (VM) managementsystem according to an exemplary embodiment of the present invention anda diagram illustrating the relationship between the VM management systemand physical servers;

FIG. 2 is a conceptual diagram schematically illustrating a VM workloadprediction method according to an exemplary embodiment of the presentinvention;

FIG. 3 is a flowchart of the VM workload prediction method according tothe exemplary embodiment of the present invention;

FIG. 4 is a diagram illustrating a storage load classification operationand a prediction model selection operation in the VM workload predictionmethod according to the exemplary embodiment of the present invention;

FIG. 5 is a diagram illustrating the prediction model selectionoperation in the VM workload prediction method according to theexemplary embodiment of the present invention;

FIG. 6 is a diagram illustrating a target prediction load informationcalculation operation in the VM workload prediction method according tothe exemplary embodiment of the present invention;

FIG. 7 is a flowchart of a VM deployment method which is a part of theVM management method according to an exemplary embodiment of the presentinvention;

FIG. 8 is a set of graphs illustrating effects achieved with a VMdeployment in consideration of a first objective function in the VMdeployment method according to the exemplary embodiment of the presentinvention;

FIG. 9 is a set of graphs illustrating effects achieved with a VMdeployment in consideration of a second objective function in the VMdeployment method according to the exemplary embodiment of the presentinvention;

FIG. 10 is a diagram illustrating a deployment schedule calculationoperation in the VM deployment method according to the exemplaryembodiment of the present invention;

FIG. 11 is a graph illustrating a method of calculating a predictionerror in the VM deployment method according to the exemplary embodimentof the present invention;

FIG. 12 is a flowchart of a VM deployment simulation method according toan exemplary embodiment of the present invention;

FIG. 13 is a diagram illustrating an operation of inputting informationin the VM deployment simulation method according to the exemplaryembodiment of the present invention;

FIG. 14 is a diagram illustrating a process of setting a selecteddeployment in the VM deployment simulation method according to theexemplary embodiment of the present invention; and

FIG. 15 is a diagram illustrating a process of calculating anddisplaying economic efficiency information and comparative informationin the VM deployment simulation method according to the exemplaryembodiment of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, exemplary embodiments of the present invention will bedescribed in detail with reference to the accompanying drawings.However, the spirit of the present invention is not limited to theembodiments set forth herein. Those of ordinary skill in the art caneasily propose other embodiments included in the scope of a regressiveinvention or the present invention by adding, changing, removing, etc.elements within the scope of the present invention, but the embodimentsalso fall within the scope of the present invention.

Throughout the drawings of embodiments, elements having the samefunctionality within the same spirit will be indicated by the samereference signs.

FIG. 1 shows a conceptual diagram of a virtual machine (VM) managementsystem according to an exemplary embodiment of the present invention anda diagram illustrating the relationship between the VM management systemand physical servers.

The VM management system according to the exemplary embodiment of thepresent invention may include a prediction device A10, a VM deploymentdevice B10, and a VM deployment simulation device C10.

The VM management system may control, regulate, and manage physicalservers.

The physical servers may indicate hardware platforms on which VMs may beexecuted.

For example, the physical servers may be general desktop computers,server computers, laptop computers, mobile phones, or the like.

However, the physical servers are not limited thereto and may be anysystem on which VMs may be executed.

The physical servers may include a first physical server to an n^(th)physical server (n is a natural number which is greater than or equal totwo).

Referring to FIG. 1 , the physical servers include a first physicalserver 20, a second physical server 30, and a third physical server 40.On the first physical server 20, a first VM 21, a second VM 22, and athird VM 23 may be run. On the second physical server 30, a fourth VM 31and a fifth VM 32 may be run. On the third physical server 40, a sixthVM 41 and a seventh VM 42 may be run.

One example is shown in FIG. 1 , and the number of physical servers orthe deployment of VMs may be variously changed within a rangeself-evident to those of ordinary skill in the art.

In the following description, it is assumed that the VM managementsystem controls, regulates, and manages the first physical server 20,the second physical server 30, and the third physical server 40.

The VM management system may perform a VM management method.

The VM management system may include a Prediction Device, a VMDeployment Device and a VM Deployment Simulation Device.

The VM management method may include a VM workload prediction method, aVM deployment method based on scheduling, and a VM deployment simulationmethod.

Each device and the VM management method will be described in detailbelow.

1. VM Workload Prediction Method and Prediction Device for Implementingthe Same

Referring to FIG. 1 , the prediction device A10 according to theexemplary embodiment of the present invention may include a predictioncontroller A11 for computing, calculating, and/or processing information(e.g., data) required for implementing the VM workload predictionmethod, a prediction storage A12 for storing the information requiredfor implementing the VM workload prediction method, a predictionreceiver A13 for receiving certain information, and a predictiontransmitter A14 for transmitting certain information.

The prediction device A10 may further include a prediction display A16for displaying an image under the control of the prediction controllerA11 and a prediction input section A15 for transferring a certain signalgenerated by an external input to the prediction controller A11, theprediction storage A12, the prediction transmitter A14, the predictionreceiver A13, and/or the prediction display A16.

The prediction controller A11 may perform computations, such asprocessing all information required for implementing the VM workloadprediction method, on the basis of the information stored in theprediction storage A12.

For example, the prediction controller A11 may indicate a centralprocessing unit (CPU).

However, the prediction controller A11 is not limited thereto and mayinclude any device or part which may perform computations required forimplementing the VM workload prediction method.

In the prediction storage A12, information (data) required forimplementing the VM workload prediction method may be stored.

For example, storage load information to be described below may bestored in the prediction storage A12.

A program for performing the VM workload prediction method may be storedin the prediction storage A12.

As an example, the prediction storage A12 may be a hard disk as anauxiliary storage device.

However, the prediction storage A12 is not limited thereto and mayinclude any element in which information required for implementing theVM workload prediction method may be stored.

The prediction receiver A13 may receive and transfer certain informationto the prediction controller A11, the prediction storage A12, theprediction input section A15, the prediction display A16, and/or theprediction transmitter A14.

For example, the prediction receiver A13 may receive informationtransferred from a physical server.

The prediction receiver A13 may receive information related to operationof the physical server from the physical server.

For example, the prediction receiver A13 may receive and transferstorage load information which will be described below to the predictionstorage A12.

The prediction transmitter A14 may transmit certain information to anarbitrary device or server.

For example, the prediction transmitter A14 may transfer the informationto the physical server.

The prediction transmitter A14 may transfer a control signal calculatedby the prediction controller A11 to the physical server.

For example, the prediction input section A15 may be a keyboard.

However, the prediction input section A15 is not limited thereto, andthe type of prediction input section A15 may be varied within a rangeself-evident to those of ordinary skill in the art.

For example, the prediction display A16 may be a display.

However, the prediction display A16 is not limited thereto, and the typeof prediction display A16 may be varied within a range self-evident tothose of ordinary skill in the art.

The elements in the prediction device A10 may be connected to each otherin a wired and/or wireless manner.

The prediction device A10 may be connected to each of the physicalservers in a wired and/or wireless manner.

Main characteristics of the VM workload prediction method implemented bythe prediction device will be described below.

FIG. 2 is a conceptual diagram schematically illustrating a VM workloadprediction method according to an exemplary embodiment of the presentinvention.

The receiver (prediction receiver) may receive information related toVMs running on physical servers and transfer the received information tothe storage.

The controller (prediction controller) may calculate storage loadinformation (VM workload information) according to a predeterminedworkload calculation method on the basis of the information related toVMs running on physical servers.

The storage load information may indicate information on storage loadswhich are loads on physical servers imposed by VMs.

The storage load information may be stored in the storage (predictionstorage).

For example, the predetermined workload calculation method may be amethod of calculating storage load information on the basis of thedegree of operation of the CPU (CPU utilization) of a physical serverresulting from running VMs.

In other words, the degree of operation of the CPU of a physical serverresulting from running VMs may denote a storage load.

In the predetermined workload calculation method, it may be assumed thatone VM is executed on only one physical server.

For example, this may denote that one VM is not implemented on twophysical servers.

A physical server workload may indicate a load assigned to a physicalserver.

For example, the physical server workload may be calculated as a loadassigned to a CPU of the physical server.

For example, the physical server workload may be calculated by addingloads on the physical server caused by VMs deployed on the physicalserver.

In this way, basic data required for prediction is simplified so thatthe accuracy of prediction in an operation to be described below may befurther increased.

A VM workload may indicate a physical server load caused by a VM runningon a physical server.

On the basis of storage loads stored in the storage, the controller maycalculate a prediction model for calculating a predicted load, whichwill be described below, using an algorithm, such as a multipleregression model, autoregressive integrated moving average (ARIMA)analysis, or deep learning.

A machine learning algorithm for generating a prediction model is atechnique well known to those of ordinary skill in the art, and thus thedetailed description thereof is omitted.

When all storage load information is input and then one prediction modelis generated through machine learning, the accuracy of prediction may bereduced, and a time period for model forming may be slightly increased.

To prevent this, before a prediction model is generated, storage loadinformation may be classified by a predetermined classification methodso that storage load information of similar characteristics may beclassified into one group.

As an example, referring to FIG. 2 , a storage load T10 may beclassified into a first group G10, a second group G20, and a third groupG30.

For the first group G10, the controller may produce a first predictionmodel M10 for predicting a load on a physical server expected to becaused by a VM through machine learning.

Likewise, for the second group G20, the controller may produce a secondprediction model M20 for predicting a load on a physical server expectedto be caused by a VM through machine learning.

Also, for the third group G30, the controller may produce a thirdprediction model M30 for predicting a load on a physical server expectedto be caused by a VM through machine learning.

As described above, prediction models are separately generated and usedon the basis of storage loads having similar characteristics orpatterns. Therefore, it is possible to increase the accuracy ofprediction and drastically reduce an analysis time.

FIG. 2 has been described on the assumption that storage loads areclassified into three groups. However, the present invention is notlimited thereto, and the classified types of groups may be varied withina range self-evident to those of ordinary skill in the art.

The VM workload prediction method implemented by the prediction devicewill be described in detail below.

FIG. 3 is a flowchart of the VM workload prediction method according tothe exemplary embodiment of the present invention.

Referring to FIG. 3 , the VM workload prediction method according to theexemplary embodiment of the present invention is implemented by theprediction device to calculate a prediction load, which is a load on aphysical server predicted to be imposed by a plurality of VMs installedon the physical server. The VM workload prediction method includes anoperation S120 of collecting storage load information which isinformation related to a storage load corresponding to a load on aphysical server caused by the VMs installed on the physical server, anoperation S130 of classifying storage loads of the VMs into groups by apredetermined classification method, an operation S140 of forming, whenthe storage loads are arranged on the basis of a certain reference, aprediction model for calculating a prediction load through deep learningbased on the storage loads from storage loads classified into eachgroup, an operation S150 of selecting at least one of a plurality ofprediction models by a predetermined selection method to increaseprediction accuracy of a target prediction load which is a load on thephysical server predicted to be caused by the a target VM to beanalyzed, and an operation S160 of calculating target prediction loadinformation which is information related to the target prediction loadaccording to a predetermined calculation method on the basis of targetstorage load information, which is information related to a targetstorage load corresponding to a load on the physical server caused bythe target VM, and a selection model which is the selected predictionmodel.

The controller may calculate the storage load information on the basisof information stored in the storage according to the predeterminedworkload calculation method (S110).

Alternatively, the receiver may receive and transfer the storage loadinformation to the storage, and the storage may store the storage loadinformation.

The controller may collect the storage load information stored in thestorage (S120).

The controller may classify the collected storage load information bythe predetermined classification method (S130).

The predetermined classification method may be a method of arranging thestorage loads on the basis of a certain arrangement reference.

FIG. 4 is a diagram illustrating a storage load classification operationand a prediction model selection operation in the VM workload predictionmethod according to the exemplary embodiment of the present invention.

The controller may arrange the storage load information according to thepredetermined classification method.

For example, the arrangement reference may be a reference related to thelength of a task time and change rates of storage loads during the tasktime.

For example, referring to FIG. 4 , the x-axis may denote the length of atask time, and the y-axis may denote the average of change rates ofstorage loads during the task time.

A time period in which the workload of a VM is a certain value or moremay be defined as a task time.

However, the present invention is not limited to the example of anarrangement reference, and the arrangement reference may be variouslychanged according to the purpose of use, the environment of use, and thelike within a range self-evident to those of ordinary skill in the art.

According to the predetermined classification method, the controller maycalculate the storage load information with respect to the arrangementreference and then calculate coordinates of one point on a graph basedon the arrangement reference.

In other words, arrangement based on an arrangement reference may be forcalculating an arrangement reference for storage loads and calculatingpositions (coordinates) of the storage loads on a graph on the basis ofthe arrangement reference.

The controller may classify the storage loads which are arranged on thebasis of the arrangement reference into groups by a referenceclassification method.

As an example, the reference classification method may be a method ofgrouping pieces of storage load information through K-means clustering.

K-means clustering is well known to those of ordinary skill in the art,and thus the detailed description thereof is omitted.

When the reference classification method is a grouping method based onK-means clustering, the number of groups for classification may bestored in advance in the controller or the storage.

Alternatively, the number of groups for classification may be input froma user through the input section (prediction input section).

As another example, the reference classification method may be a methodof grouping pieces of storage load information through hierarchicalclustering.

Hierarchical clustering is well known to those of ordinary skill in theart, and thus the detailed description thereof is omitted.

When the reference classification method is a grouping method based onhierarchical clustering, the number of groups for classification may belikewise stored in advance in the controller or the storage.

Alternatively, the number of groups for classification may be input fromthe user through the input section.

The reference classification method will be described below on theassumption that pieces of storage load information are grouped throughhierarchical clustering, but the present invention is not limitedthereto.

Referring to FIG. 4 , the controller may arrange the storage loadsaccording to the predetermined classification method.

For example, when a predetermined number of groups is three, thecontroller may classify the storage loads into three groups (the firstgroup G10, the second group G20, and the third group G30).

In this case, pieces of storage load information may not be included inany group.

When the storage loads are arranged on the basis of the certainarrangement reference, the controller may produce a prediction model forcalculating a prediction load on the basis of the storage loadinformation from storage loads classified into each group.

For example, the controller may perform machine learning with thestorage loads of each group to produce each prediction model forpredicting a future VM workload.

For example, when the number of groups is three, three prediction modelsmay be produced.

As an example, a prediction model produced by performing machinelearning with storage load information included in the first group G10may be referred to as a first prediction model.

As another example, a prediction model produced by performing machinelearning with storage load information included in the second group G20may be referred to as a second prediction model.

As another example, a prediction model produced by performing machinelearning with storage load information included in the third group G30may be referred to as a third prediction model.

After prediction models are produced, a future load of an arbitrary VMmay be predicted.

A target VM may indicate a VM to be analyzed.

When a target VM is selected, the controller may calculate targetstorage load information according to the predetermined workloadcalculation method on the basis of information which is stored in thestorage and related to an operation of a physical server caused by thetarget VM.

The target storage load may indicate a load on the physical servercaused by the target VM, and the target storage load information mayindicate information related to the target storage load.

The controller may select at least one of a plurality of predictionmodels on the basis of the calculated target load information accordingto the predetermined selection method.

The predetermined selection method may include a first selection methodof selecting, when the target storage load is located in a group ofstorage loads after the target storage load is arranged on the basis ofthe arrangement reference, a prediction model produced on the basis ofthe storage loads classified into the group in which the target storageload is located as the selection model.

In other words, a prediction model produced on the basis of storage loadinformation corresponding to the group may be selected as the selectionmodel.

When the target storage load is located in a group of storage loadsafter the target storage load is arranged on the basis of thearrangement reference, one prediction model may be selected as theselection model.

As a specific example, referring to FIG. 4 , when the target storageload is arranged on the basis of the arrangement reference, the targetstorage load may be located in a group J10. Assuming that the targetstorage load is located in the first group G10, the first predictionmodel may be selected as the selection model.

FIG. 5 is a diagram illustrating the prediction model selectionoperation in the VM workload prediction method according to theexemplary embodiment of the present invention.

The predetermined selection method may further include a secondselection method of selecting, when the target storage load is notlocated in any group of storage loads after the target storage load isarranged on the basis of the arrangement reference, prediction modelscorresponding to groups of storage loads satisfying a predeterminedcondition as the selection model.

As an exemplary embodiment of the present invention, the predeterminedcondition may require the group to be located within a certain distancefrom the target storage load when the target storage load is arranged onthe basis of the arrangement reference.

As another exemplary embodiment of the present invention, thepredetermined condition may require the group to be located within areference range from a reference distance which is the shortest one ofdistances from the target storage load to the groups of storage loadswhen the target storage load is arranged on the basis of the arrangementreference.

Specifically, referring to FIG. 5 , when the storage loads areclassified by the predetermined classification method, it may be assumedthat the storage loads are classified into the first group G10, thesecond group G20, and the third group G30.

The target storage load may be arranged on the basis of the arrangementreference by the controller.

For example, the distance between a position J10 of the target storageload and the first group G10 may be 1, the distance between the positionJ10 of the target storage load and the second group G20 may be 1.5, andthe distance between the position J10 of the target storage load and thethird group G30 may be 2.

Under the predetermined condition according to an exemplary embodimentof the present invention, the first prediction model and the secondprediction model may be selected as the selection model because thefirst prediction model and the second prediction model correspond to thefirst group G10 and the second group G20 present within the certaindistance (e.g., 1.7) from the target storage load.

Alternatively, under the predetermined condition according to anotherexemplary embodiment of the present invention, the first predictionmodel and the second prediction model may be selected as the selectionmodel because the first prediction model and the second prediction modelcorrespond to the first group G10 and the second group G20 presentwithin a reference range (e.g., 0.7) from a reference distance (in theexample, 1) which is the shortest one of distances from the targetstorage load to the storage load groups.

The above example has been described on the assumption that the distancebetween a target storage load and a group is the distance from theposition of the target storage load and the center of the group.

However, the present invention is not limited thereto, and the method ofcalculating the distance between a target storage load and a group maybe changed. The method of calculating the distance between an entity anda group is self-evident to those of ordinary skill in the art, and thusthe detailed description thereof is omitted.

The controller may calculate target prediction load information relatedto the target prediction load on the basis of the selection model andthe target storage load information according to the predeterminedcalculation method.

The predetermined calculation method may be a method of calculating thetarget prediction load information in different ways depending onwhether the selection model is singular or plural.

Specifically, the predetermined calculation method may be a method ofcalculating the target prediction load information by inputting thetarget storage load information to the selection model when theselection model is singular.

Also, the predetermined calculation method may be a method ofcalculating, when the selection model has a plurality of selectionmodels, the target prediction load information by considering weights,which are calculated on the basis of a ratio of separation distancesfrom the target storage load to storage load groups corresponding to theselection models after the target storage load is arranged on the basisof the arrangement reference.

Specifically, when the selection model has a plurality of selectionmodels, the target storage load information may be input to eachselection model.

When the target storage load information is input to the selectionmodel, the controller may calculate a plurality of pieces of targetpreliminary load information related to a plurality of targetpreliminary loads as a result value.

The target preliminary loads may indicate a predicted load of the targetVM on a physical server which is calculated by inputting the targetstorage load information to one prediction model among the selectionmodels.

Also, the controller may calculate weights on the basis of distancesbetween the target storage load and the selection models.

The controller may calculate the target prediction load information bymerging the target preliminary loads on the basis of the weights.

The greater the distance between the target storage load and theselection model, the smaller the weight.

The smaller the distance between the target storage load and theselection model, the larger the weight.

As an example, referring to FIG. 4 , when only the first predictionmodel is selected as the selection model, the target storage loadinformation is input only to the first prediction model according to thepredetermined calculation method so that the target prediction loadinformation may be calculated.

As another example, referring to FIG. 5 , when the first predictionmodel and the second prediction model are selected as the selectionmodel, the target storage load information is input to each of the firstprediction model and the second prediction model according to thepredetermined calculation method so that the target preliminary loadinformation related to target preliminary loads may be calculated.

For example, the target storage load information may be input to thefirst prediction model so that first target preliminary load informationrelated to a first target preliminary load, which is a targetpreliminary load related to the first prediction model, may becalculated.

For example, the target storage load information may be input to thesecond prediction model so that second target preliminary loadinformation related to a second target preliminary load, which is atarget preliminary load related to the second prediction model, may becalculated.

The controller may merge the first target preliminary load and thesecond target preliminary load on the basis of the first targetpreliminary load information and the second target preliminary loadinformation.

In this case, boosting or bagging may be used as a merging method.

These techniques are well known to those of ordinary skill in the art,and the detailed description thereof is omitted.

Since the first group is closer to the position of the target storageload than the second group, the first target preliminary loadinformation may have a higher weight than the second target preliminaryload information when merged with the second target preliminary loadinformation.

FIG. 6 is a diagram illustrating a target prediction load informationcalculation operation in the VM workload prediction method according tothe exemplary embodiment of the present invention.

As a specific example, a first target preliminary load P10 and a secondtarget preliminary load P20 may be calculated as shown in the graph ofFIG. 6 .

The first group is closer to the position of the target storage loadthan the second group. Therefore, when the first target preliminary loadP10 and the second target preliminary load P20 are merged, a targetprediction load U10 may be calculated to be biased toward the firsttarget preliminary load P10.

As described above, a greater weight is given to a target preliminaryload resulting from a closer group, and thus prediction accuracy can befurther increased.

The predetermined calculation method may further include a method ofcalculating target preliminary load information of a certain timeperiod.

The predetermined calculation method may calculate a workload of a VMfor each time period.

Accordingly, it is possible to readily predict a future workload andeasily find a solution for managing a data center thereafter.

The controller may calculate target prediction load information of allVMs present in the physical servers.

The user may migrate VMs on the basis of future workloads of VMs toprevent the physical servers from being overloaded and to efficientlyrun the physical servers.

A target VM may be one VM on the above-described physical servers or anexternal VM which is wanted to be analyzed.

2. VM Deployment Method Based on Scheduling and VM Deployment Device forImplementing the Same

Referring to FIG. 1 , the VM deployment device B10 according to theexemplary embodiment of the present invention may include a deploymentprocessor B11 for processing information (e.g., data) required fordeploying and arranging VMs, a deployment storage B12 for storing theinformation required for deploying and arranging VMs, a deploymentreceiver B13 for receiving the information required for deploying andarranging VMs, and a deployment transmitter B14 for transmitting theinformation required for deploying and arranging VMs.

Also, the VM deployment device B10 may further include a deploymentinput section B15 for transferring information generated by externalinputs to the deployment receiver B13 and a deployment display B16 fordisplaying a deployment schedule, which will be describe below, tonotify the user of the deployment schedule.

The deployment processor B11 may perform computations, such asprocessing all information required for implementing the VM deploymentmethod, on the basis of the information stored in the storage(deployment storage).

For example, the deployment processor B11 may indicate a CPU.

However, the deployment processor B11 is not limited thereto and mayinclude any device or part which may perform computations required forimplementing the VM deployment method.

In the deployment storage B12, information (data) required forimplementing the VM deployment method may be stored.

A program for performing the VM deployment method may be stored in thedeployment storage B12.

For example, the deployment storage B12 may be a hard disk as anauxiliary storage device.

However, the deployment storage B12 is not limited thereto and mayinclude any element in which information required for implementing theVM deployment method may be stored.

For example, the deployment storage B12 may be a database.

The deployment receiver B13 may receive information from a physicalserver.

The deployment receiver B13 may receive information related to operationof the physical server from the physical server.

The deployment transmitter B14 may transmit information to the physicalserver.

The deployment transmitter B14 may transfer a control signal calculatedby the deployment processor B11 to the physical server.

Accordingly, the VM deployment device B10 may transfer a control signalto each physical server so that a VM running on one physical server maybe migrated to another physical server.

For example, the deployment input section B15 may be a keyboard.

However, the deployment input section B15 is not limited thereto, andthe type of deployment input section B15 may be varied within a rangeself-evident to those of ordinary skill in the art.

For example, the deployment display B16 may be a display.

However, the deployment display B16 is not limited thereto, and the typeof deployment display B16 may be varied within a range self-evident tothose of ordinary skill in the art.

The elements in the VM deployment device B10 may be connected to eachother in a wired and/or wireless manner.

The VM deployment device B10 may be connected to each of the physicalservers in a wired and/or wireless manner.

The VM deployment method implemented by the VM deployment device B10will be described in detail below.

FIG. 7 is a flowchart of a VM deployment method which is a part of theVM management method according to the exemplary embodiment of thepresent invention.

Referring to FIG. 7 , the VM deployment method according to theexemplary embodiment of the present invention is a method of deploying aplurality of VMs on a first physical server and a second physical serverto efficiently run a physical server, on which VMs are installed,including the first physical server and the second physical server. TheVM deployment method includes an operation S210 of calculating VMworkload information, which is information related to VM workloadscorresponding to loads on the physical servers imposed by the VMsaccording to a predetermined workload calculation method on the basis ofinformation related to operation of the first physical server and thesecond physical server, an operation S220 of calculating initiallypredicted virtual load information which is information related to aninitially predicted virtual load associated with loads on the physicalserver predicted to be imposed by the VMs during a predetermined firsttime period on the basis of the VM workloads, an operation S230 ofcalculating deployment schedule information, which is informationrelated to a deployment schedule of the VMs on the first physical serverand the second physical server, according to a first predeterminedmachine deployment calculation method, and an operation S260 ofmigrating and deploying the VMs on the first physical server and thesecond physical server so that the VMs may be deployed on the firstphysical server and the second physical server according to thedeployment schedule calculated by the first predetermined machinedeployment calculation method.

The VM deployment method may further include an operation S240 ofdisplaying a plurality of deployment schedules calculated according tothe first predetermined machine deployment calculation method throughthe display.

The VM deployment method may further include an operation S250 ofreceiving a selected deployment schedule, which is one of the pluralityof deployment schedules displayed through the display, through the inputsection (deployment input section) and transferring information relatedto the selected deployment schedule to the processor (deploymentprocessor).

The VM deployment method may further include an operation S270 ofdetermining, when the VMs are deployed on the first physical server andthe second physical server according to the selected deploymentschedule, whether selectively predicted virtual load information, whichis information related to a selectively predicted virtual loadassociated with predicted loads on the physical servers, is calculatednormally according to a predetermined check method on the basis of theselectively predicted virtual load information and actual virtualworkload information which is information related to an actual workloadof the VMs.

The storage may store information related to operation of the firstphysical server, information related to operation of the second physicalserver, and information related to operation of the third physicalserver,

For example, the information related to operation of the first physicalserver, the second physical server, and the third physical server may bestored in the storage in a time-series manner.

To this end, the receiver (deployment receiver) may receive theinformation related to operation of each of the first physical server,the second physical server, and the third physical server.

For example, operation-related information may be information related tooperating ratios of elements of a physical server.

The processor may calculate may calculate VM workload information, whichis information related to VM workloads corresponding to loads on thephysical servers imposed by the VMs according to a predeterminedworkload calculation method on the basis of information stored in thestorage and related to operation of each of the physical servers (S210).

A VM workload may indicate a load on resources of a physical server whena VM is run on the physical server.

The VM workload may indicate a load assigned to the physical server whenthe VM is run on the physical server.

For example, the VM workload may be calculated as a load which isimposed on a CPU of the physical server by the VM running on thephysical server.

Consequently, the predetermined workload calculation method may includea method of calculating a VM workload as a load assigned to a CPU of aphysical server.

In other words, according to the predetermined workload calculationmethod, the VM workload information may be calculated on the basis ofonly the degree of operation of a CPU.

In the predetermined workload calculation method, it may be assumed thatone VM is executed on only one physical server.

For example, this may denote that one VM is not implemented on twophysical servers.

A physical server workload may indicate a load assigned to a physicalserver.

For example, the physical server workload may be calculated as a loadassigned to a CPU of the physical server.

For example, the physical server workload may be calculated by addingloads on the physical server caused by VMs deployed on the physicalserver.

In this way, basic data which is described below and required forprediction is simplified so that the accuracy of prediction in anoperation to be described below may be further increased.

The processor may calculate initially predicted virtual load information(target prediction load information) corresponding to informationrelated to an initially predicted virtual load associated with loads onthe physical servers predicted to be imposed by the VMs during thepredetermined first time period, which is a future time period, from areference time point on the basis of the VM workloads (S220).

For example, the reference time point may be a time point for VMworkload information calculation.

However, the reference time point is not limited thereto and may bevariously changed within a range self-evident to those of ordinary skillin the art.

To predict a load of a VM, the above-described VM workload predictionmethod may be used.

Therefore, the initially predicted virtual load information may indicatetarget prediction load information.

However, the initially predicted virtual load information is not limitedthereto and may be calculated using a method other than the VM workloadprediction method.

To calculate the initially predicted virtual load information, VMworkload information, which is a workload of each of the VMs during thepredetermined first time period, may be predicted.

Loads of the VMs corresponding to the predetermined first time periodare predicted, and the VMs are redeployed on the basis of the predictedloads. This is because, when the VM are migrated on the basis ofoverload corresponding to a specific time point, the number ofmigrations is increased, and it is not possible to stabilize the overallphysical servers.

Therefore, the above problem can be effectively solved by deploying VMson physical servers on the basis of a specific time period rather than aspecific time point in the future.

For example, the predetermined first time period may be three months.

However, the present invention is not limited thereto, and thepredetermined first time period may be variously changed within a rangeself-evident to those of ordinary skill in the art.

For example, the processor may calculate VM workload information of thepredetermined first time period using an algorithm, such as a multipleregression model, ARIMA analysis, or deep learning, on the basis of theVM workload information stored in the storage.

The processor may calculate a predicted workload on a physical serverfor the predetermined first time period on the basis of the VM workloadinformation of the predetermined first time period.

This is a technique well-known to those of ordinary skill in the art,and the detailed description thereof is omitted.

For example, the initially predicted virtual load may indicate aworkload of a VM predicted for the predetermined first time period fromthe time point for VM workload information calculation or thepredetermined first time period from an arbitrary time point.

The processor may calculate deployment schedule information which isinformation related to deployment schedules of the VMs on the firstphysical server and the second physical server according to the firstpredetermined machine deployment calculation method (S230).

The first predetermined machine deployment calculation method mayindicate a method of calculating a deployment schedule of the VMs tominimize the value of an objective function on the basis of theinitially predicted virtual load information.

The first predetermined machine deployment calculation method mayinclude a method of calculating a deployment schedule of the VMs so thata load may be assigned to an arbitrary physical server only within apredetermined range (first condition).

For example, the predetermined range may be an operating ratio of aphysical server from 10% to 80%.

The operating ratio of a physical server means to the amount of assignedloads to the physical sever.

However, the predetermined range is not limited thereto and may bevariously changed within a range self-evident to those of ordinary skillin the art.

The first predetermined machine deployment calculation method mayinclude a method of moving at least one of VMs deployed on the arbitraryphysical server to another physical server so that a load may beassigned to the arbitrary physical server only within the predeterminedrange when a load on the arbitrary physical server is predicted to be afirst reference X10 (see FIGS. 8 and 9 ) or more (first-firstcondition).

For example, the first reference X10 (see FIGS. 8 and 9 ) may requirethe operating ratio of a physical server to be 80%.

The processor may calculate the deployment schedule using a method ofmigrating at least one VM deployed on the target physical server toanother physical server at a time point at which a load on the targetphysical server is predicted to exceed the first reference as a solutionfor preventing a load on the physical server from exceeding the firstreference.

Accordingly, it is possible to effectively prevent a load on thephysical server from exceeding the first reference, that is, an overloadon the physical server.

However, the first reference is not limited thereto and may be variouslychanged within a range self-evident to those of ordinary skill in theart.

Also, the first predetermined machine deployment calculation method mayinclude a method of moving all VMs deployed on the arbitrary physicalserver to another physical server so that a load may be assigned to thearbitrary physical server only within the predetermined range when aload on the arbitrary physical server is predicted to be a secondreference X20 (see FIGS. 8 and 9 ) or less (first-second condition).

For example, the second reference X20 (see FIGS. 8 and 9 ) may requirethe operating ratio of a physical server to be 10%.

The processor may calculate the deployment schedule using a method ofmigrating all VMs deployed on the target physical server to anotherphysical server at a time point at which a load on the target physicalserver is predicted to be the second reference or less as a solution forpreventing a load on the physical server from decreasing to the secondreference or less.

This may denote a start condition for stopping operation of a physicalserver.

In other words, as a solution for preventing a load on the physicalserver from decreasing to the second reference or less, the processormay stop operation of the target physical server by migrating a VMdeployed on the target physical server to another server.

The first predetermined machine deployment calculation method mayinclude a method of calculating the deployment schedule information sothat an operating physical server may not be switched to a non-operatingstate due to migration of a VM which causes a non-operating physicalserver to operate (second condition).

When one physical server starts operating as soon as another physicalserver is stopped, there is no benefit in stopping the other physicalserver, and rather, more power and time are consumed to operate theserver.

Therefore, such a problem may be solved by the second condition.

This will be described in detail below.

When a deployment schedule is calculated according to the firstpredetermined machine deployment calculation method, the deploymentschedule may be calculated to allow migration of a VM between physicalservers during the predetermined first time period (third condition).

However, data may be lost due to frequent migration of a VM.Consequently, migration of one VM during the predetermined first timeperiod may be limited to a certain number of times (fourth condition).

For example, the certain number of times may be two.

However, the certain number of times is not limited thereto and may bevariously changed within a range self-evident to those of ordinary skillin the art.

In addition, when a deployment schedule is calculated according to thefirst predetermined machine deployment calculation method, physicalservers may be assumed to have the same specification (fifth condition(homogeneous condition)).

The objective function may include a first objective function, a secondobjective function, a third objective function, and a fourth objectivefunction.

The processor may calculate four non-dominated solutions in which theobjective functions have minimum values.

Therefore, according to the first predetermined machine deploymentcalculation method, four deployment schedules may be calculated.

The first predetermined machine deployment calculation method will bedescribed in detail below.

FIG. 8 is a set of graphs illustrating effects achieved with a VMdeployment in consideration of a first objective function in the VMdeployment method according to the exemplary embodiment of the presentinvention.

Referring to FIG. 8 , the objective function may include the firstobjective function which is a function related to a load variation of aphysical server calculated with the initially predicted virtual loadduring the predetermined first time period.

For example, FIG. 8A is a set of graphs showing loads on the firstphysical server to the third physical server when VMs are deployed onthe first physical server to the third physical server according to afirst schedule.

Also, FIG. 8B is a set of graphs showing loads on the first physicalserver to the third physical server when VMs are deployed on the firstphysical server to the third physical server according to a secondschedule.

It has been described above that each physical server workload may becalculated by adding workloads of VMs installed on each physical server.

Referring to FIG. 8A, when VMs are deployed on the physical serversaccording to the first schedule, all physical server workloads may beless than the first reference X10 and greater than the second referenceX20.

Also, referring to FIG. 8B, when VMs are deployed on the physicalservers according to the second schedule, all physical server workloadsmay be less than the first reference X10 and greater than the secondreference X20.

However, when VMs are deployed on the physical servers on the basis ofthe second schedule, a variation (slope) of each physical serverworkload may be smaller than that of a case in which VMs are deployed onthe physical servers on the basis of the first schedule.

Accordingly, as a deployment schedule of VMs on the physical servers,the second schedule may be better than the first schedule.

Also, a value of the first objective function for deriving the secondschedule may be smaller than a value of the first objective function forderiving the first schedule.

When a physical server workload has a high change rate, the physicalserver workload may exceed an arbitrary reference within only a shorttime period, and migration of a VM may occur.

In this case, as described above, migration of a VM may frequentlyoccur, and thus a risk of data loss of the VM may be further increased.

The first objective function may be a function for preventing the risk.

FIG. 9 is a set of graphs illustrating effects achieved with a VMdeployment in consideration of a second objective function in the VMdeployment method according to the exemplary embodiment of the presentinvention.

Referring to FIG. 9 , the objective function may include the secondobjective function which is a function related to a difference betweenloads on the physical servers (physical server workloads) calculatedwith the initially predicted virtual load during the predetermined firsttime period.

For example, FIG. 9A is a set of graphs showing loads on the firstphysical server to the third physical server when VMs are deployed onthe first physical server to the third physical server according to athird schedule.

Also, FIG. 9B is a set of graphs showing loads on the first physicalserver to the third physical server when VMs are deployed on the firstphysical server to the third physical server according to a fourthschedule.

It has been described above that each physical server workload may becalculated by adding workloads of VMs installed on each physical server.

Referring to FIG. 9A, when VMs are deployed on the physical serversaccording to the third schedule, all physical server workloads may beless than the first reference X10 and greater than the second referenceX20.

Also, referring to FIG. 9B, when VMs are deployed on the physicalservers according to the fourth schedule, all physical server workloadsmay be less than the first reference X10 and greater than the secondreference X20.

However, when VMs are deployed on the physical servers on the basis ofthe fourth schedule, a difference (slope) between physical serverworkloads may be smaller than that of a case in which VMs are deployedon the physical servers on the basis of the third schedule.

Accordingly, as a deployment schedule of VMs on the physical servers,the fourth schedule may be better than the third schedule.

Also, a value of the second objective function for deriving the fourthschedule may be smaller than a value of the second objective functionfor deriving the third schedule.

To compare physical server workloads with each other, the physicalservers may be compared with each other using an average value of loadsof the physical servers corresponding to the predetermined first timeperiod.

The second objective function may be intended to evenly use physicalservers.

The objective function may include the third objective function which isa function related to the number of times that VMs are migrated on thefirst physical server and the second physical server so as to bedeployed according to the deployment schedule.

In other words, the third objective function may be related to thenumber of times that VMs in a current deployment are migrated betweenphysical servers so as to be deployed according to a deploymentschedule.

The third objective function may be intended to find a deployment inwhich the number of times that VMs are migrated is minimized.

Each migration of a VM has a risk of data loss.

The third objective function may be intended to minimize the probabilityof data loss.

The objective function may include the fourth objective function whichis a function related to whether the first physical server operatesduring the predetermined first time period and whether the secondphysical server operates during the predetermined first time period.

In other words, the fourth objective function may be a function relatedto whether a physical server operates during the predetermined firsttime period.

When a value of the fourth objective function is reduced, the number ofnon-operating physical servers and a non-operating time of physicalservers may be maximized.

When a value of the fourth objective function is minimized, the sum ofnon-operating times of all physical servers may be maximized.

The fourth objective function may serve to minimize power consumption ofphysical servers by minimizing an operating time of the physicalservers.

A deployment schedule may indicate a schedule about how VMs are deployedon physical servers to minimize a value of each objective function whilethe first to fifth conditions are satisfied.

The objective functions may include the first objective function whichis a function related to a load variation of a physical servercalculated with the initially predicted virtual load during thepredetermined first time period, the second objective function which isa function related to a difference between loads on physical serverscalculated with the initially predicted virtual load during thepredetermined first time period, the third objective function which is afunction related to the number of times that the VMs are migrated on thefirst physical server and the second physical server to be deployedaccording to the deployment schedule, and the fourth objective functionwhich is a function related to whether the first physical serveroperates during the predetermined first time period and whether thesecond physical server operates during the predetermined first timeperiod.

The display (deployment display) may display the deployment schedulestransferred from the processor (S240).

The processor may calculate first deployment schedule information whichis information related to a first deployment schedule, second deploymentschedule information which is information related to a second deploymentschedule, third deployment schedule information which is informationrelated to a third deployment schedule, and fourth deployment scheduleinformation which is information related to a fourth deploymentschedule.

The deployment schedules may include the first deployment schedule whichis a deployment of VMs on physical servers (e.g., the first physicalserver and the second physical server) for minimizing the value of thesecond objective function, the value of the third objective function,and the value of the fourth objective function when the first objectivefunction has a minimum value, the second deployment schedule which is adeployment of the VMs on physical servers (e.g., the first physicalserver and the second physical server) for minimizing the value of thefirst objective function, the value of the third objective function, andthe value of the fourth objective function when the second objectivefunction has a minimum value, the third deployment schedule which is adeployment of the VMs on physical servers (e.g., the first physicalserver and the second physical server) for minimizing the value of thefirst objective function, the value of the second objective function,and the value of the fourth objective function when the third objectivefunction has a minimum value, and the fourth deployment schedule whichis a deployment of the VMs on physical servers (e.g., the first physicalserver and the second physical server) for minimizing the value of thefirst objective function, the value of the second objective function,and the value of the third objective function when the fourth objectivefunction has a minimum value.

The first predetermined machine deployment calculation method will bedescribed below with an example of one deployment schedule.

FIG. 10 is a diagram illustrating a deployment schedule calculationoperation in the VM deployment method according to the exemplaryembodiment of the present invention.

FIG. 10 shows schedules of VMs deployed on physical servers during acertain time period in the predetermined first time period.

As an example, FIG. 10 may relate to the first deployment schedule forminimizing a value of the first objective function, and the firstdeployment schedule will be described below on the basis of thisexample.

However, this is only an example, and the present invention is notlimited thereto.

For example, physical servers may include the first physical server 20,the second physical server 30, and the third physical server 40, VMs mayinclude a first VM 91, a second VM 92, a third VM 93, a fourth VM 94, afifth VM 95, a sixth VM 96, and a seventh VM 97.

For example, to minimize values of the other objective functions while avalue of the first objective function is minimized, the processor maycalculate deployment schedules so that the first VM 91 and the second VM92 may be deployed on the first physical server 20, so that the third VM93, the fourth VM 94, and the fifth VM 95 may be deployed on the secondphysical server 30, and so that the sixth VM 96 and the seventh VM 97may be deployed on the third physical server 40 during a zeroth timeperiod which is the preceding time period of a first time period.

According to the VM deployment of the zeroth time period, a load of thefirst physical server 20 may be predicted to be the second reference orless during the first time period.

In this case, the first-second condition is satisfied, and a deploymentschedule may be calculated so that the first VM 91 and the second VM 92deployed on the first physical server 20 may be migrated to anotherserver.

When VMs are migrated under the first-second condition, a physicalserver to which the VMs are migrated may be determined so that a valueof the first objective function may be minimized.

On the basis of the determination, a deployment schedule may becalculated so that the first VM 91 may be migrated to the secondphysical server 30 and the second VM 92 may be migrated to the thirdphysical server 40.

The deployment schedule may be calculated so that the first physicalserver 20 may not operate during a second time period due to themigration during the first time period.

For example, assuming that, when the third VM 93, the fourth VM 94, thefifth VM 95, and the first VM 91 are deployed on the second physicalserver 30 and the second VM 92, the sixth VM 96, and the seventh VM 97are deployed on the third physical server 40, the other objectivefunctions are minimized while the first objective function is minimized,no migration may occur during the second time period.

To minimize values of the other objective functions while a value of thefirst objective function is minimized, the deployment schedule may becalculated so that the first VM 91 deployed on the second physicalserver 30 may be migrated to the third physical server 40 during a thirdtime period.

Also, to minimize values of the other objective functions while a valueof the first objective function is minimized, the deployment schedulemay be calculated so that the fifth VM 95 deployed on the secondphysical server 30 may be migrated to the third physical server 40during a fourth time period.

During the second time period to the fourth time period, the firstphysical server 20 may not operate.

This may denote that the deployment schedule is calculated so that thefirst physical server 20 may not operate during the second time periodto the fourth time period under the influence of the fourth objectivefunction.

According to the VM deployment of the fourth time period, a load of thethird physical server 40 may be predicted to be the first reference ormore during the fifth time period.

In this case, the deployment schedule may be calculated so that at leastone of the VMs deployed on the third physical server 40 may be migratedto another physical server during the fifth time period (first-firstcondition).

According to the VM deployment of the fourth time period, a load of thesecond physical server 30 may be predicted to be the first reference ormore during the fifth time period (first-first condition).

In this case, the deployment schedule may be calculated so that at leastone of the VMs deployed on the second physical server 30 may be migratedto another physical server during the fifth time period (first-firstcondition).

However, the first VM 91 deployed on the third physical server 40 ismigrated during the first time period and the third time period and thusmay be excluded from migration targets during the fifth time period(fourth condition).

Also, when both of the third VM 93 and the fourth VM 94 deployed on thesecond physical server 30 are migrated to the first physical server 20,the second physical server 30 stops operating, and the first physicalserver 20 may start operating.

This does not satisfy the second condition, and thus only one of thethird VM 93 and the fourth VM 94 may be migrated from the secondphysical server 30 to the first physical server 20.

For example, to minimize values of the other objective functions while avalue of the first objective function is minimized, the first conditionto the fourth condition (specifically, the first-second condition andthe second condition) may be satisfied. To this end, the third VM 93 maybe migrated from the second physical server 30 to the first physicalserver 20, and the sixth VM 96 and the seventh VM 97 may be migratedfrom the third physical server 40 to the first physical server 20.

The deployment schedule may be calculated so that no migration may occurduring a sixth time period because minimum values of the objectivefunctions are satisfied due to the migration during the fifth timeperiod.

Although the first deployment schedule has been described above, this isonly an example, and detailed descriptions of methods of calculating aschedule on the basis of the second deployment schedule, the thirddeployment schedule, and the fourth deployment schedule may be omittedwhen the detailed descriptions overlap with the above description.

The display may display the first deployment schedule, the seconddeployment schedule, the third deployment schedule, and the fourthdeployment schedule.

The four objective functions are separate functions and may have fournon-dominated solutions. Consequently, the first deployment schedule,the second deployment schedule, the third deployment schedule, and thefourth deployment schedule may indicate separate solutions.

A worker may determine one of the first deployment schedule, the seconddeployment schedule, the third deployment schedule, and the fourthdeployment schedule and select and input the determined deploymentschedule.

The input section may calculate information related to the selecteddeployment schedule and transfer the information to the processor.

FIG. 11 is a graph illustrating a method of calculating a predictionerror in the VM deployment method according to the exemplary embodimentof the present invention.

The processor may migrate and deploy VMs on physical servers on thebasis of the selected deployment schedule (S260).

In this way, resources of the physical servers may not be concentratedin one physical server and may be used evenly.

In addition, the VM deployment method according to the exemplaryembodiment of the present invention may further include an operationS270 of determining, when the VMs are deployed on the first physicalserver and the second physical server according to the deploymentschedule, whether selectively predicted virtual load information, whichis information related to a selectively predicted virtual loadassociated with predicted loads on the physical servers, is calculatednormally according to the predetermined check method on the basis of theselectively predicted virtual load information and actual virtualworkload information which is information related to an actual workloadcorresponding to loads actually imposed on the physical servers by theVMs.

The selectively predicted virtual load information may be identical tothe initially predicted virtual load information.

However, the selectively predicted virtual load information is notlimited thereto and may not be identical to the initially predictedvirtual load information.

In this case, the processor may deploy the VMs according to the selecteddeployment schedule and then calculate selectively predicted virtualload information by predicting loads of the VMs for a certain timeperiod.

The predetermined check method may be a method of determining that theselectively predicted virtual load is calculated inaccurately when adifference between the actual workload of the VMs and the selectivelypredicted virtual load information corresponding thereto is a certainvalue or more.

As a specific example, FIG. 11 may be a graph of a first workload Y10representing a selectively predicted virtual load which is a predictedload of a first VM and a second workload Y20 representing an actualworkload of the first VM.

When a difference between the first workload and the second workload isa certain load (certain value) Y or more, the processor may determinethat the load of the first VM is predicted inaccurately.

When it is determined that the selectively predicted virtual load iscalculated inaccurately, the processor may calculate re-predictedvirtual load information, which is information related to a re-predictedvirtual load associated with a load on a physical server predicted to becaused by the VM, during a predetermined second time period from aredetermination time point which is a time point at which it isdetermined that the selectively predicted virtual load is calculatedinaccurately (S280).

The processor may recalculate a deployment schedule according to theabove-described first predetermined machine deployment calculationmethod on the basis of the re-predicted virtual load information (S280)and may perform a certain computation so that the VMs may be redeployedon the basis of the recalculated deployment schedule (S290).

The detailed description thereof may be omitted when overlapping theabove description.

The predetermined second time period may be identical to or differentfrom the predetermined first time period.

Such a VM deployment method may be repeatedly and continuouslyperformed.

The worker may select one of a plurality of deployment schedules toredeploy the VMs (S290), and the processor may redeploy the VMs on thebasis of the selected deployment schedule (S290).

The detailed description thereof may be omitted when overlapping theabove description.

A VM deployment device according to an exemplary embodiment of thepresent invention implements a VM deployment method of deploying aplurality of VMs on a first physical server and a second physical serverto efficiently run a physical server, on which VMs are installed,including the first physical server and the second physical server. TheVM deployment device includes a processor and a storage in which aprogram for performing the VM deployment method is stored.

The VM deployment method includes an operation of calculating VMworkload information, which is information related to VM workloadscorresponding to loads on the physical servers imposed by the VMs,according to the predetermined workload calculation method on the basisof information related to operation of the first physical server and thesecond physical server, an operation of calculating initially predictedvirtual load information which is information related to an initiallypredicted virtual load associated with loads on the physical serverspredicted to be imposed by the VMs during a predetermined first timeperiod on the basis of the VM workloads, an operation of calculatingdeployment schedule information, which is information related to adeployment schedule of the VMs on the first physical server and thesecond physical server, according to a first predetermined machinedeployment calculation method, and an operation of migrating anddeploying the VMs on the first physical server and the second physicalserver so that the VMs may be deployed on the first physical server andthe second physical server according to the deployment schedulecalculated by the first predetermined machine deployment calculationmethod. The first predetermined machine deployment calculation methodmay be a method of calculating the deployment schedule of the VMs tominimize an objective function value on the basis of the initiallypredicted virtual load information.

3. VM Deployment Simulation Method and VM Deployment Simulation Devicefor Implementing the Same

Referring to FIG. 1 , the VM deployment simulation device C10 accordingto the exemplary embodiment of the present invention may include asimulation processor C11 for processing information (e.g., data)required for performing a VM deployment simulation method, a simulationstorage C12 for storing the information required for performing the VMdeployment simulation method, a simulation input section C13 forgenerating a signal related to the information required for performingthe VM deployment simulation method by a user's manipulation, asimulation transceiver C14 for transmitting and receiving theinformation required for performing the VM deployment simulation method,a simulation display C15 for displaying an image, and a simulationcontroller C16 for controlling the simulation processor C11, thesimulation storage C12, the simulation input section C13, the simulationtransceiver C14, and/or the simulation display C15 so that the VMdeployment simulation method may be smoothly performed.

The simulation processor C11 may perform computations, such asprocessing all information required for implementing the VM deploymentsimulation method, on the basis of the information stored in thesimulation storage C12.

For example, the simulation processor C11 may indicate a CPU.

However, the simulation processor C11 is not limited thereto and mayinclude any device or part which may perform computations required forimplementing the VM deployment simulation method.

In the simulation storage C12, information (data) required forimplementing the VM deployment simulation method may be stored.

A program for performing the VM deployment simulation method may bestored in the simulation storage C12.

For example, the simulation storage C12 may be a hard disk as anauxiliary storage device.

However, the simulation storage C12 is not limited thereto and mayinclude any element in which information required for implementing theVM deployment simulation method may be stored.

For example, the simulation storage C12 may be a database.

The deployment transceiver C14 may receive information or transferinformation through an arbitrary medium.

For example, the simulation transceiver C14 may receive information froma physical server or transfer information to a physical server.

As an example, the simulation transceiver C14 may receive informationdirectly from the physical server but may receive information on thephysical server through a separate storage medium.

The simulation transceiver C14 may receive information to be receivedthrough another element provided in the VM deployment simulation deviceC10.

Alternatively, the simulation transceiver C14 may receive certaininformation from another element provided in the VM deploymentsimulation device C10.

The simulation input section C13 may generate an input signal bymanipulation of the user.

When the input signal is received by the simulation processor C11, thesimulation processor C11 may generate information corresponding to thereceived input signal.

For example, the simulation input section C13 may be a keyboard.

However, the simulation input section C13 is not limited thereto, andthe type of simulation input section C13 may be varied within a rangeself-evident to those of ordinary skill in the art.

The simulation display C15 (identical to a display section in thefollowing description) may display a certain image under the control ofthe simulation controller C16.

For example, the display C15 may be a display.

However, the display C15 is not limited thereto, and the type of displayC15 may be varied within a range self-evident to those of ordinary skillin the art.

The simulation controller C16 may control the simulation processor C11,the simulation storage C12, the simulation input section C13, and thesimulation transceiver C14 so that the VM deployment simulation deviceC10 may smoothly perform the VM deployment simulation method.

The elements in the VM deployment simulation device C10 may be connectedto each other in a wired and/or wireless manner.

Each of the elements in the VM deployment simulation device C10 may beconnected to the physical servers in a wired and/or wireless manner.

The VM deployment simulation method implemented by the VM deploymentsimulation device C10 will be described in detail below.

FIG. 12 is a flowchart of a VM deployment simulation method according toan exemplary embodiment of the present invention.

Referring to FIG. 12 , the VM deployment simulation method according tothe exemplary embodiment of the present invention may include anoperation S310 of calculating designation information, which ispreviously designated information required for a simulation, in responseto a signal generated by the input section, an operation S320 ofcalculating VM workload information, which is information related to VMworkload information corresponding to loads on the physical serversimposed by the VMs, according to the predetermined workload calculationmethod on the basis of information stored in the storage (simulationstorage) and related to operation of the physical servers, an operationS330 of calculating observed state information which is informationrelated to states of the first physical server and the second physicalserver during a first time period and calculated on the basis ofinformation that is stored in the storage and related to operation ofthe first physical server and the second physical server, and anoperation S340 of calculating initially predicted virtual loadinformation, which is information related to an initially predictedvirtual load associated with loads on the physical servers predicted tobe imposed by the VMs during the second time period, according to apredetermined prediction method using the information stored in thestorage and related to operation of the first physical server and thesecond physical server.

Also, the VM deployment simulation method according to the exemplaryembodiment of the present invention may further include an operationS350 of calculating machine deployment information, which is informationrelated to a machine deployment corresponding to a deployment of the VMson the first physical server and the second physical server calculatedaccording to a predetermined machine deployment calculation method andan operation S360 of calculating predicted state information, which isinformation related to states of the first physical server and thesecond physical server during a second time period, on the basis of theinitially predicted virtual load information when the VMs are deployedon the first physical server and the second physical server according tothe machine deployment.

In addition, the VM deployment simulation method according to theexemplary embodiment of the present invention may include an operationS370 of calculating candidate predicted state information which isinformation related to a value within a range of a maximum to a minimumof predicted state values calculated on the basis of the plurality ofpieces of predicted state information, an operation of displaying acandidate predicted state value calculated on the basis of the predictedstate value and the candidate predicted state information on the displaysection according to a predetermined display method, an operation S380of calculating selected deployment information related to a selecteddeployment which is a deployment selected as a new deployment of the VMson the first physical server and the second physical server, anoperation S390 of calculating economic efficiency information which isinformation related to economic benefit acquired when the VMs aredeployed in the selected deployment in relation to a case in which theVMs are deployed in a past deployment, and an operation S400 ofcalculating comparative information on the basis of the observed stateinformation and the predicted state information, which is informationrelated to states of the first physical server and the second physicalserver during the second time period and calculated on the basis of theinformation that is stored in the storage and related to operation ofthe first physical server and the second physical server, to compare acase in which the VMs are deployed in the selected deployment selectedon the basis of the predicted state information with a case in which theVMs are deployed in the past deployment corresponding to a deployment ofthe VMs which is a base of the observed state information.

Each operation will be described in detail below.

FIG. 13 is a diagram illustrating an operation of inputting informationin the VM deployment simulation method according to the exemplaryembodiment of the present invention.

Referring to FIG. 13 , the user may input values displayed on a screenof a display section 15 through the input section.

The user may select a physical server and/or a VM of which designationinformation will be input by the user.

The processor (simulation processor) may calculate the designationinformation by the user's manipulation of the input section.

The designation information is information required for a simulation andmay be previously designated by the user.

For example, the designation information may include information relatedto specifications H10 of each physical server.

For example, the user may input a CPU value H11, a memory value H12, anetwork input/output (I/O) value H13, and/or a disk I/O value H14.

For example, the designation information may include information relatedto specifications Q10 of each VM.

For example, the user may input a CPU ratio Q11 allocated to anarbitrary VM on a physical server and/or a memory ratio Q12 allocated tothe arbitrary VM through the input section.

For example, the designation information may include information relatedto an objective function used in the predetermined machine deploymentcalculation method.

For example, lists of objective functions which may be used in thepredetermined machine deployment calculation method may be displayed onthe display section 15, and the user may select an objective function tobe used in the predetermined machine deployment calculation method.

The designation information may be calculated by the user's selection.

For example, the objective function may include a first objectivefunction F11 which is a function related to a load variation of aphysical server, a second objective function F12 which is a functionrelated to a difference between loads on physical servers, a thirdobjective function F14 which is a function related to the number oftimes that the VMs are migrated on physical servers, and a fourthobjective function F13 which is a function related to whether thephysical servers are operating.

The objective functions will be described in detail below.

FIG. 14 is a diagram illustrating a process of setting a selecteddeployment in the VM deployment simulation method according to theexemplary embodiment of the present invention.

After all input values displayed in the display are input, a simulationmay be performed.

In the storage, information related to operation of the physical serversmay be stored.

The processor may calculate the VM workload information on the basis ofthe information related to operation of the physical server according tothe predetermined workload calculation method.

A VM workload may indicate a load on resources of a physical server whena VM is run on the physical server.

The VM workload may indicate a load assigned to the physical server whenthe VM is run on the physical server.

For example, the VM workload may be calculated as a load which isimposed on a CPU of the physical server by the VM running on thephysical server.

Consequently, the predetermined workload calculation method may includea method of calculating a VM workload as a load assigned to a CPU of aphysical server.

In other words, according to the predetermined workload calculationmethod, the VM workload information may be calculated on the basis ofonly the degree of operation of a CPU (CPU utilization).

In the predetermined workload calculation method, it may be assumed thatone VM is executed on only one physical server.

As an example, this may denote that one VM is not implemented on twophysical servers.

A VM workload may indicate a load assigned to a physical server by arunning VM.

A physical server workload may indicate a load assigned to the physicalserver.

For example, the physical server workload may be calculated as a loadassigned to a CPU of the physical server.

For example, the physical server workload may be calculated by addingloads on the physical server imposed by VMs deployed on the physicalserver.

In this way, basic data which is described below and required forprediction is simplified so that the accuracy of prediction in anoperation to be described below may be further increased.

The processor may calculate observed state information related to thephysical servers operating in the first time period.

The observed state information may include observed occupancyinformation which is information related to loads on the physicalservers imposed by the VMs during the first time period, observedviolation information which is information related to whether apredetermined reference is violated when the VMs run on the physicalservers during the first time period, observed idle state informationwhich is information related to a time in which the physical servers donot operate because the VMs are not deployed on the physical serversduring the first time period, and observed migration information whichis information related to the number of times that the VMs are migratedduring the first time period.

The observed occupancy information may be information related to valuesof loads on the physical servers imposed by the VMs during the firsttime period.

For example, referring to FIG. 14 , a load (physical server workload)value of each physical server may be represented as an average graphW11, a maximum value graph W12, and a minimum value graph W13 on thedisplay section.

The average graph W11 may indicate a graph showing the average of loadsassigned to each physical server during the first time period.

The maximum value graph W12 may indicate a graph showing only maximumvalues extracted from loads assigned to each physical server during thefirst time period.

The minimum value graph W13 may indicate a graph showing only minimumvalues extracted from loads assigned to each physical server during thefirst time period.

From such graphs, the user can clearly grasp the degree of operation ofa physical server so far.

The observed occupancy information may be calculated on the basis of theVM workload information.

The observed violation information may be information about whether apredetermined reference is violated when the physical servers on whichthe VMs are deployed operate.

The predetermined reference may indicate a certain reference based onwhich the physical servers are recommended to operate.

For example, the predetermined reference may include a reference that aload assigned to the physical server is 90% or less of a total load.

However, the predetermined reference is not limited thereto and may bevariously changed and added according to a mode, purpose, and place ofuse.

For example, referring to FIG. 14 , an observed violation value E11calculated on the basis of the observed violation information may bedisplayed on the display section.

The observed idle state information may be information related to a timein which the physical servers do not operate because the VMs are notdeployed on the physical servers.

The observed idle state information may be calculated as the sum of idletimes of all the physical servers.

For example, referring to FIG. 14 , an observed idle state value R11calculated on the basis of the observed idle state information may bedisplayed on the display section.

The observed migration information may be information related to thenumber of times that the VMs are migrated on the physical servers.

The observed migration information may be calculated as the sum of thenumbers of migrations on all the physical servers.

For example, referring to FIG. 14 , an observed migration value T11calculated on the basis of the observed migration information may bedisplayed on the display section.

For example, referring to FIG. 14 , values related to the observed stateinformation and values related to the predicted state information may besimultaneously displayed on the display section 15.

In this way, the user can clearly compare states before a change andstates after the change and make a judgement.

A process for calculating the predicted state information will bedescribed in detail below.

The processor may calculate initially predicted virtual load informationrelated to an initially predicted virtual load associated with loads onthe physical servers predicted to be imposed by the VMs during thepredetermined second time period, which is a future time period,according to the predetermined prediction method using the VM workloadinformation.

In other words, this may be a process of predicting what workloads onthe physical servers will be imposed by the VMs in the future on thebasis of process data stored in the storage.

For example, the predetermined prediction method may be a method ofcalculating VM workload information of the second time period using analgorithm, such as a multiple regression model, ARIMA analysis, or deeplearning, on the basis of the VM workload information.

However, the predetermined prediction method is not limited thereto andmay be any well-known technique for implementing a method of predictinga future workload. The detailed description thereof is self-evident tothose of ordinary skill in the art and may be omitted.

For example, the initially predicted virtual load may be a VM workloadwhich is predicted for the second time period from the time point for VMworkload information calculation or the second time period from anarbitrary time point.

The processor may calculate the machine deployment information accordingto a predetermined machine deployment calculation method on the basis ofthe calculated initially predicted virtual load information.

The predetermined machine deployment calculation method may be identicalto the above-described predetermined machine deployment calculationmethod, and the detailed description thereof may be omitted whenoverlapping the above description.

The number of machine deployments may vary depending on the number ofobjective functions designated by the user.

For example, when three objective functions are designated by the user,three machine deployments may be calculated.

This may be because the objective functions have solutions which are notdominated by each other.

The processor may calculate predicted state information of a case inwhich the VMs are deployed on the physical servers according to each ofthe machine deployments.

In other words, when there are a plurality of objective functions, aplurality of pieces of predicted state information may be generated.

The predicted state information may be calculated on the basis of theinitially predicted virtual load information.

The predicted state information may include at least one of predictedoccupancy information which is information related to loads on thephysical servers predicted to be imposed by the VMs during the secondtime period, predicted violation information which is informationrelated to whether the predetermined reference is violated when the VMsare assumed to run in an arbitrary deployment on the physical serversduring the second time period, predicted idle state information which isinformation related to a time in which the physical servers do notoperate because the VMs are not deployed on the physical servers duringthe second time period, and predicted migration information which isinformation related to the number of times that the VMs are migratedduring the second time period.

The predicted occupancy information may be information related to avalue of a load on the physical servers imposed by the VMs during thesecond time period.

For example, a load value of each physical server may be represented asan average graph Z11, a maximum value graph Z12, and a minimum valuegraph Z13 on the display section.

The average graph Z11 may indicate a graph showing the average of loadsassigned to each physical server during the second time period.

The maximum value graph Z12 may indicate a graph showing only maximumvalues extracted from loads assigned to each physical server during thesecond time period.

The minimum value graph Z13 may indicate a graph showing only minimumvalues extracted from loads assigned to each physical server during thesecond time period.

From such graphs, the user can clearly find the future degree ofoperation of a physical server.

The observed occupancy information may be calculated on the basis of theVM workload information.

The predicted violation information may be information about whether thepredetermined reference is violated when the physical servers operateduring the second time period.

A predicted violation value C11 which is a value of the predictedviolation information may be displayed on the display section.

The detailed description of the predetermined reference may be omittedwhen overlapping the above description.

The predicted idle state information may be information related to atime in which the physical servers do not operate because the VMs arenot deployed on the physical servers during the second time period.

The predicted idle state information may be calculated as the sum ofidle times of all the physical servers.

The predicted migration information may be information related to thenumber of times that the VMs are migrated on the physical servers duringthe second time period.

The predicted migration information may be calculated as the sum of thenumbers of migrations on all the physical servers.

The processor may calculate predicted state information for each of themachine deployments.

An input mark for selecting each machine deployment may be displayed onthe display section.

On the basis of machine deployment information input through the displaysection, predicted state information corresponding to the machinedeployment information may be displayed on the display section.

For example, referring to FIG. 14 , it is assumed that there are fourmachine deployments because the user designates four objectivefunctions.

When the user clicks a first deployment, the display section may displayvalues related to predicted state information of a case in which the VMsare deployed in the first deployment during the second time period.

On the other hand, when the user clicks a second deployment, the displaysection may display values related to predicted state information of acase in which the VMs are deployed in the second deployment during thesecond time period.

However, regardless of the user's machine deployment selection, thedisplay section may display predicted idle state values D11, D12, D13,and D14 related to predicted idle state information and/or predictedmigration values N11, N12, N13, and N14 related to predicted migrationinformation.

The predicted idle state values and the predicted migration valuesrelated to the VM deployment designated by the user may be displayed asa mark L10 on the display section.

The processor may calculate candidate predicted state information on thebasis of the predicted state information.

Specifically, the processor may calculate first candidate predictedstate information on the basis of the predicted idle state informationcorresponding to each machine deployment.

As for the first candidate predicted state information, a predicted idlestate value corresponding to each machine deployment may be calculated.

In this case, values between a maximum predicted idle state value and aminimum predicted idle state value may be first candidate predictedstate values, and data related to the first candidate predicted statevalues may be the first candidate predicted state information.

Specifically, referring to FIG. 14 , it is assumed that the userdesignates the four objective functions. In the case of the first VMdeployment, the predicted idle state value D11 may be 2.3 days. In thecase of the second VM deployment, the predicted idle state value D12 maybe 1.1 days. In the case of the third VM deployment, the predicted idlestate value D13 may be 4.3 days. In the case of the fourth VMdeployment, the predicted idle state value D14 may be 5 days.

The maximum predicted idle state value may be the predicted idle statevalue D14 of the fourth VM deployment, and the minimum predicted idlestate value may be the predicted idle state value D12 of the second VMdeployment.

Therefore, a first candidate predicted state value D15 may be a valuebetween 1.1 days and 5 days excluding the predicted idle state value ofthe first VM deployment and the predicted idle state value of the thirdVM deployment.

Also, the processor may calculate second candidate predicted stateinformation on the basis of predicted migration informationcorresponding to each machine deployment.

Likewise, a predicted migration value corresponding to each machinedeployment may be calculated.

In this case, values between a maximum predicted migration value and aminimum predicted migration value may be second candidate predictedstate values, and data related to the second candidate predicted statevalues may be the second candidate predicted state information.

Specifically, referring to FIG. 14 , it is assumed that the userdesignates the four objective functions. In the case of the first VMdeployment, the predicted migration value N11 may be 15 times. In thecase of the second VM deployment, the predicted migration value N12 maybe 12 times. In the case of the third VM deployment, the predictedmigration value N13 may be 23 times. In the case of the fourth VMdeployment, the predicted migration value N14 may be 7 times.

The maximum predicted migration value may be the predicted migrationvalue N13 of the fourth VM deployment, and the minimum predicted idlestate value may be the predicted migration value N14 of the fourth VMdeployment.

Therefore, a second candidate predicted state value N15 may be a valuebetween 7 times and 23 times excluding the predicted migration value N12of the second VM deployment and the predicted migration value N11 of thefirst VM deployment.

The processor may display the candidate predicted state values D15 andN15 on the display section according to a predetermined display method.

The predetermined display method may be a method of displaying predictedstate values and candidate predicted state values as a certain range(e.g., a bar format).

The predicted state values may be values calculated on the basis of thepredicted state information.

As a specific example, referring to FIG. 14 , the predetermined displaymethod may include a method of displaying the predicted idle statevalues D11, D12, D13, and D14 and the first candidate predicted statevalue D15 as a certain range.

Also, referring to FIG. 14 , the predetermined display method mayinclude a method of displaying the predicted migration values N11, N12,N13, and N14 and the second candidate predicted state value N15 as acertain range.

In this way, the user can effectively compare states of the physicalservers when the VMs are deployed in the separate machine deployments.

The user may select one VM deployment among the machine deployments.

FIG. 15 is a diagram illustrating a process of calculating anddisplaying economic efficiency information and comparative informationin the VM deployment simulation method according to the exemplaryembodiment of the present invention.

When the user selects a VM deployment, selected deployment informationmay be calculated.

When the user selects a VM deployment, information related to a selecteddeployment schedule may be calculated.

The processor may calculate economic efficiency information on the basisof the selected deployment information related to a selected deploymentwhich is the VM deployment selected by the user.

The economic efficiency information may indicate data related toeconomic indicators.

The economic indicators relating to the economic efficiency informationmay be indicators of economic benefit acquired when the VMs are deployedin the selected deployment in relation to a case in which the VMs aredeployed in the past deployment.

For example, the economic indicators may include a return on investment(ROI), a net present value (NPV), an internal rate of return (IRR), abreakeven point (BEP), and the like.

For example, the processor may calculate a cost for running the physicalservers on the basis of operating time of the physical servers and anelectricity bill per unit when the VMs are deployed in the pastdeployment on the physical servers.

Also, the processor may calculate an expected cost for running thephysical servers on the basis of operating time of the physical serversand an electricity bill per unit when the VMs are deployed in theselected deployment on the physical servers.

The processor may calculate the economic indicators by calculating theamount of benefit on the basis of the costs.

In other words, the economic efficiency information may be calculated onthe basis of the observed idle state information and the predicted idlestate information.

Information on the amount of investment may be stored in the storage inadvance.

Alternatively, the information on the amount of investment may begenerated by a signal generated from the input section.

Also, the processor may calculate comparative information for comparingthe case in which the VMs are deployed in the selected deployment duringthe second time period with the case in which the VMs are deployed inthe past deployment during the first time period.

The comparative information may be related to information onimprovements achieved when the VMs are deployed in the selecteddeployment on the physical servers.

The display section may display comparative values G11 calculated on thebasis of the comparative information.

Also, the display section may display economic indicator values K11,K12, K13, and K14 calculated with the economic indicators.

For example, the comparative information may include informationcalculated on the basis of the observed occupancy information and thepredicted occupancy information.

For example, the comparative information may be information relating toimprovements in workloads on the physical servers.

For example, the comparative information may be information calculatedon the basis of observed violation information and the predictedviolation information.

For example, the comparative information may include informationcalculated on the basis of an observed violation value and a predictedviolation value.

For example, when the observed violation value is 10 and the predictedviolation value is 5, a comparative value calculated on the basis of theobserved violation information and the predicted violation informationmay be 5.

Likewise, the comparative information may be information calculated onthe basis of the observed idle state information and the predicted idlestate information.

In other words, the comparative information may include informationcalculated on the basis of an observed idle state value and a predictedidle state value.

Also, the comparative information may be information calculated on thebasis of the observed migration information and the predicted migrationinformation.

In other words, the comparative information may include informationcalculated on the basis of an observed migration value and a predictedmigration value.

When looking at comparative values displayed on the display section, theuser can easily recognize economic benefit which is acquired when the VMare deployed in the selective deployment.

Also, the display section may display predicted state valuescorresponding to the case in which the VMs are deployed in the selecteddeployment on the physical servers together with the comparative values.

The first time period may indicate a certain time period before thesimulation is performed.

Information related to operation of the physical servers during thefirst time period may be stored in the storage.

Using the above-described simulation method, it is possible to analyzeVM deployment effects in advance without redeploying VMs.

The predetermined machine deployment calculation method will bedescribed in detail below.

The predetermined machine deployment calculation method may include afirst predetermined machine deployment calculation method in whichmigration of VMs is allowed during a migration time period (the secondtime period) and a second predetermined machine deployment calculationmethod in which migration of VMs is not taken into consideration duringthe migration time period.

The user may select a type of predetermined machine deploymentcalculation method to be used in the previous information inputoperation.

The detailed description of the first predetermined machine deploymentcalculation method may be omitted below when overlapping the abovedescription.

The detailed description of the objective function may be omitted belowwhen overlapping the above description.

The second predetermined machine deployment calculation method will bedescribed in detail below.

The processor may calculate machine deployment information which isinformation related to a machine deployment corresponding to adeployment of the VMs on the first physical server and the secondphysical server according to the second predetermined machine deploymentcalculation method.

When a machine deployment is calculated according to the secondpredetermined machine deployment calculation method, it may be assumedthat the physical servers have homogeneous specifications.

The first condition to the fourth condition applied to the firstpredetermined machine deployment calculation method may not be appliedto the second predetermined machine deployment calculation method.

In the second predetermined machine deployment calculation method, onlythe first objective function, the second objective function, and thethird objective function may be taken into consideration.

The detailed description of the second predetermined machine deploymentcalculation method may be omitted when overlapping that of the firstpredetermined machine deployment calculation method.

Technical characteristics of each of the prediction device, the VMdeployment device, and the VM deployment simulation device may beapplied to the other devices.

As an example, the technical characteristics of the prediction devicemay be applied to the VM deployment device.

Also, technical characteristics of each of the VM workload predictionmethod, the VM deployment method based on scheduling, and the VMdeployment simulation method may be applied to the other methods.

As an example, the technical characteristics of the VM workloadprediction method may be applied to the VM deployment simulation method.

The VM management method and system for implementing the VM managementmethod according to the present invention make it possible to predict afuture VM workload with high accuracy.

Also, it is possible to predict a future VM workload in a short time.

Also, it is possible to accurately reflect a purpose of a user.

Also, it is possible to stably manage a data center.

Also, it is possible to minimize data loss.

Also, it is possible to minimize resource consumption.

Also, it is possible to obtain results in advance through a simulation.

However, effects of the present invention are not limited to thosementioned above, and effects not mentioned above will be clearlyunderstood by those of ordinary skill in the art from this specificationand the accompanying drawings.

Throughout the appended drawings, elements which are irrelevant to thetechnical spirit of the present invention have been briefly described oromitted to clearly represent the technical spirit of the presentinvention.

Although the above configuration and characteristics of the presentinvention have been described with exemplary embodiments, those ofordinary skill in the art should understand that various modificationsor changes can be made within the spirit and scope of the presentinvention. Therefore, it is noted that such modifications or changesfall within the claims.

What is claimed is:
 1. A virtual machine management method of simulatinga change in deployment of virtual machines deployed on physical serversincluding a first physical server and a second physical serverphysically separated from the first physical server, the virtual machinemanagement method including a virtual machine deployment simulationmethod, wherein the virtual machine deployment simulation methodcomprises: a simulation processor calculating observed state informationwhich is information related to states of the first physical server andthe second physical server during a first time period, the observedstate information calculated on the basis of information that is storedin a storage and related to operation of the first physical server andthe second physical server; the simulation processor calculatinginitially predicted virtual load information, which is informationrelated to an initially predicted virtual load associated with loads onthe physical servers predicted to be imposed by the virtual machinesduring the second time period, using the information stored in thestorage and related to operation of the first physical server and thesecond physical server according to a predetermined prediction method;the simulation processor calculating machine deployment informationwhich is information related to a machine deployment corresponding to adeployment of the virtual machines on the first physical server and thesecond physical server calculated according to the predetermined machinedeployment calculation method; the simulation processor calculatingselected deployment information related to a selected deployment whichis one of the machine deployments on the first physical server and thesecond physical server; and the simulation processor calculatingcomparative information on the basis of the observed state informationand predicted state information, to compare a case in which the virtualmachines are deployed in the selected deployment selected with a case inwhich the virtual machines are deployed in a past deploymentcorresponding to a deployment of the virtual machines which is a base ofthe observed state information; the simulation processor calculating acandidate predicted state information which is information related to avalue within a range of a maximum to a minimum of predicted state valuescalculated on the basis of a plurality of pieces of predicted stateinformation; and the simulation processor displaying a candidatepredicted state value calculated on the basis of the predicted statevalues and the candidate predicted state information according to apredetermined display method; wherein predicted state information iscalculated based on the initially predicted virtual load information,wherein the predetermined machine deployment calculation method is amethod of calculating a plurality of machine deployments, which aredeployments of the virtual machines when values of a plurality ofobjective functions are minimized, on the basis of the initiallypredicted virtual load information, and wherein the predetermineddisplay method is a method of displaying the predicted state value andthe candidate predicted state value as a certain range by overlappingeach other; and wherein the virtual machine management method furtherincludes deploying at least one of the virtual machines on at least oneof the first physical server or the second physical server based upon aresult of the virtual machine deployment simulation method.
 2. Thevirtual machine management method of claim 1, wherein the observed stateinformation includes at least one of observed occupancy informationwhich is information related to loads on the physical servers imposed bythe virtual machines during the first time period, observed violationinformation which is information related to whether a predeterminedreference is violated when the virtual machines run on the physicalservers during the first time period, observed idle state informationwhich is information related to a time in which the physical servers donot operate because the virtual machines are not deployed on thephysical servers during the first time period, and observed migrationinformation which is information related to the number of times that thevirtual machines are migrated during the first time period, and thepredicted state information includes at least one of predicted occupancyinformation which is information related to loads on the physicalservers predicted to be imposed by the virtual machines during thesecond time period, predicted violation information which is informationrelated to whether the predetermined reference is violated when thevirtual machines are assumed to run in an arbitrary deployment on thephysical servers during the second time period, predicted idle stateinformation which is information related to a time in which the physicalservers do not operate because the virtual machines are not deployed onthe physical servers during the second time period, and predictedmigration information which is information related to the number oftimes that the virtual machines are migrated during the second timeperiod.
 3. The virtual machine management method of claim 2, wherein thevirtual machine deployment simulation method further comprisescalculating economic efficiency information which is information relatedto economic benefit acquired when the virtual machines are deployed inthe selected deployment in relation to a case in which the virtualmachines are deployed in the past deployment.
 4. The virtual machinemanagement method of claim 3, wherein the economic efficiencyinformation is calculated on the basis of the observed idle stateinformation and the predicted idle state information.
 5. A virtualmachine management method of simulating a change in deployment ofvirtual machines deployed on physical servers including a first physicalserver and a second physical server physically separated from the firstphysical server, the virtual machine management method including avirtual machine deployment simulation method, wherein the virtualmachine deployment simulation method comprises: a simulation processorcalculating observed state information which is information related tostates of the first physical server and the second physical serverduring a first time period, the observed state information calculated onthe basis of information that is stored in a storage and related tooperation of the first physical server and the second physical server;the simulation processor calculating initially predicted virtual loadinformation, which is information related to an initially predictedvirtual load associated with loads on the physical servers predicted tobe imposed by the virtual machines during the second time period, usingthe information stored in the storage and related to operation of thefirst physical server and the second physical server according to apredetermined prediction method; the simulation processor selecting anobjective function to be used in a predetermined machine deploymentcalculation method among lists of objective functions; the simulationprocessor calculating machine deployment information which isinformation related to a machine deployment corresponding to adeployment of the virtual machines on the first physical server and thesecond physical server calculated according to the predetermined machinedeployment calculation method; and the simulation processor calculatingselected deployment information related to a selected deployment whichis one of the machine deployments on the first physical server and thesecond physical server; wherein the predetermined machine deploymentcalculation method is a method of calculating the machine deployments,which are deployments of the virtual machines when the selectedobjective function is minimized; and wherein the virtual machinemanagement method further includes deploying at least one of the virtualmachines on at least one of the first physical server or the secondphysical server based upon a result of the virtual machine deploymentsimulation method.
 6. The virtual machine management method of claim 5,wherein the lists of objective functions include a first objectivefunction which is a function related to a load variation of a physicalserver calculated with the initially predicted virtual load during thesecond time period.
 7. The virtual machine management method of claim 5,wherein the lists of objective functions include a second objectivefunction which is a function related to a difference between loads onphysical servers calculated with the initially predicted virtual loadduring the second time period.
 8. A virtual machine management systemimplementing a virtual machine deployment simulation method ofsimulating a change in deployment of virtual machines deployed onphysical servers including a first physical server and a second physicalserver physically separated from the first physical server, wherein thevirtual machine management system comprises: a virtual machinedeployment simulation device, wherein the virtual machine deploymentsimulation device comprises a simulation processor for processinginformation required for performing the virtual machine deploymentsimulation method and a simulation storage for storing the informationrequired for performing the virtual deployment simulation method,wherein the simulation processor calculates observed state informationwhich is information related to states of the first physical server andthe second physical server during a first time period, the observedstate information calculated on the basis of information that is storedin the simulation storage and related to operation of the first physicalserver and the second physical server; wherein the simulation processorcalculates initially predicted virtual load information, which isinformation related to an initially predicted virtual load associatedwith loads on the physical servers predicted to be imposed by thevirtual machines during the second time period, using the informationstored in the storage and related to operation of the first physicalserver and the second physical server according to a predeterminedprediction method; wherein the simulation processor calculates machinedeployment information related to a machine deployment corresponding toa deployment of the virtual machines on the first physical server andthe second physical server calculated according to the predeterminedmachine deployment method; wherein the simulation processor calculatesselected deployment information related to a selected deployment whichis one of the machine deployments on the first physical server and thesecond physical server; wherein the simulation processor calculatescomparative information on the basis of the observed state informationand predicted state information to compare a case in which the virtualmachines are deployed in the selected deployment selected with a case inwhich the virtual machines are deployed in a past deploymentcorresponding to a deployment of the virtual machines which is a base ofthe observed state information; wherein the simulation processorcalculates a candidate predicted state information which is informationrelated to a value within a range of a maximum to a minimum of predictedstate values calculated on the basis of a plurality of pieces ofpredicted state information; wherein the simulation processor displays acandidate predicted state value calculated on the basis of the predictedstate values and the candidate predicted state information according toa predetermined display method; wherein predicted state information iscalculated based on the initially predicted virtual load information,wherein the predetermined machine deployment calculation method is amethod of calculating a plurality of machine deployments, which aredeployments of the virtual machines when values of a plurality ofobjective functions are minimized, on the basis of the initiallypredicted virtual load information, wherein the predetermined displaymethod is a method of displaying the predicted state value and thecandidate predicted state value as a certain range by overlapping eachother; and wherein the virtual machine deployment simulation devicefurther includes a simulation transceiver configured to transferinformation from the virtual deployment simulation device to the firstphysical server and/or the second physical server.